library(ggplot2)
  library(reshape2)
  library(dplyr)
  library(tidyr)
  library(GGally)
  library(grid)
    "%&%" = function(a,b) paste(a,b,sep="")
  source('/Volumes/im-lab/nas40t2/hwheeler/PrediXcan-Paper/scripts/Heather/make-figures/multiplot.R')
  my.dir <- '/Volumes/im-lab/nas40t2/hwheeler/cross-tissue/'
  out.dir <- '~/GitHub/GenArch/GenArchPaper/OTD_enrichment/'

Pull h2 estimate CI>0 genes for each tissue & rm .\d+ from ensid

for(tistype in c("Tissue-Specific","Tissue-Wide")){
  tsfile <- my.dir %&% 'GTEx_' %&% tistype %&% '_local_h2_se_geneinfo_no_description.txt'
  ts <- read.table(tsfile,sep='\t',header=T)
  tislist <- scan(my.dir %&% 'GTEx_nine_tissues','c')
  tislist <- gsub("-","",tislist)
  tislist <- c("CrossTissue",tislist)
  h2tislist <- "h2." %&% tislist
  setislist <- "se." %&% tislist
  geneinfo <- ts[,1:3]
  for(i in seq_along(tislist)){
    h2tis <- h2tislist[i]
    setis <- setislist[i]
    data <- ts %>% select(AssociatedGeneName, EnsemblGeneID, matches(h2tis), matches(setis)) %>% mutate(ensid=substr(EnsemblGeneID,1,15))
    cidata <- data[data[,3]-data[,4]*2>0,] ##pull genes with non-zero confidence intervals
    print(tislist[i] %&% " " %&% tistype %&% ": " %&% dim(cidata)[1] %&% " non-zero h2 CI genes")
    write.table(cidata, file=out.dir %&% tislist[i] %&% "_" %&% tistype %&% "_non-zeroCIgenes_info.txt",quote=FALSE,row.names = FALSE)
    write.table(cidata[,5], file=out.dir %&% tislist[i] %&% "_" %&% tistype %&% "_non-zeroCIgenes_ensid_list.txt",quote=FALSE,row.names = FALSE, col.names = FALSE)
    write.table(cidata[,1], file=out.dir %&% tislist[i] %&% "_" %&% tistype %&% "_non-zeroCIgenes_gene_list.txt",quote=FALSE,row.names = FALSE, col.names = FALSE)
    
    for(thresh in c(0,0.01,0.05,0.1)){
      h2data <- data[data[,3]>thresh,] ##pull genes with h2 > thresh
    print(tislist[i] %&% " " %&% tistype %&% ": " %&% dim(h2data)[1] %&% " h2 > " %&% thresh %&% " genes")
    write.table(h2data, file=out.dir %&% tislist[i] %&% "_" %&% tistype %&% "_h2_" %&% thresh %&% "genes_info.txt",quote=FALSE,row.names = FALSE)
    write.table(h2data[,5], file=out.dir %&% tislist[i] %&% "_" %&% tistype %&% "_h2_" %&% thresh %&% "genes_ensid_list.txt",quote=FALSE,row.names = FALSE, col.names = FALSE)
    write.table(h2data[,1], file=out.dir %&% tislist[i] %&% "_" %&% tistype %&% "_h2_" %&% thresh %&% "genes_gene_list.txt",quote=FALSE,row.names = FALSE, col.names = FALSE)
    } 
  }
}
## [1] "CrossTissue Tissue-Specific: 2938 non-zero h2 CI genes"
## [1] "CrossTissue Tissue-Specific: 17007 h2 > 0 genes"
## [1] "CrossTissue Tissue-Specific: 10282 h2 > 0.01 genes"
## [1] "CrossTissue Tissue-Specific: 5042 h2 > 0.05 genes"
## [1] "CrossTissue Tissue-Specific: 2715 h2 > 0.1 genes"
## [1] "AdiposeSubcutaneous Tissue-Specific: 207 non-zero h2 CI genes"
## [1] "AdiposeSubcutaneous Tissue-Specific: 17007 h2 > 0 genes"
## [1] "AdiposeSubcutaneous Tissue-Specific: 6908 h2 > 0.01 genes"
## [1] "AdiposeSubcutaneous Tissue-Specific: 2463 h2 > 0.05 genes"
## [1] "AdiposeSubcutaneous Tissue-Specific: 650 h2 > 0.1 genes"
## [1] "ArteryTibial Tissue-Specific: 306 non-zero h2 CI genes"
## [1] "ArteryTibial Tissue-Specific: 17007 h2 > 0 genes"
## [1] "ArteryTibial Tissue-Specific: 7779 h2 > 0.01 genes"
## [1] "ArteryTibial Tissue-Specific: 3126 h2 > 0.05 genes"
## [1] "ArteryTibial Tissue-Specific: 892 h2 > 0.1 genes"
## [1] "HeartLeftVentricle Tissue-Specific: 298 non-zero h2 CI genes"
## [1] "HeartLeftVentricle Tissue-Specific: 17007 h2 > 0 genes"
## [1] "HeartLeftVentricle Tissue-Specific: 7796 h2 > 0.01 genes"
## [1] "HeartLeftVentricle Tissue-Specific: 4012 h2 > 0.05 genes"
## [1] "HeartLeftVentricle Tissue-Specific: 1614 h2 > 0.1 genes"
## [1] "Lung Tissue-Specific: 223 non-zero h2 CI genes"
## [1] "Lung Tissue-Specific: 17007 h2 > 0 genes"
## [1] "Lung Tissue-Specific: 7143 h2 > 0.01 genes"
## [1] "Lung Tissue-Specific: 2738 h2 > 0.05 genes"
## [1] "Lung Tissue-Specific: 797 h2 > 0.1 genes"
## [1] "MuscleSkeletal Tissue-Specific: 434 non-zero h2 CI genes"
## [1] "MuscleSkeletal Tissue-Specific: 17007 h2 > 0 genes"
## [1] "MuscleSkeletal Tissue-Specific: 7371 h2 > 0.01 genes"
## [1] "MuscleSkeletal Tissue-Specific: 2356 h2 > 0.05 genes"
## [1] "MuscleSkeletal Tissue-Specific: 622 h2 > 0.1 genes"
## [1] "NerveTibial Tissue-Specific: 379 non-zero h2 CI genes"
## [1] "NerveTibial Tissue-Specific: 17006 h2 > 0 genes"
## [1] "NerveTibial Tissue-Specific: 7797 h2 > 0.01 genes"
## [1] "NerveTibial Tissue-Specific: 3491 h2 > 0.05 genes"
## [1] "NerveTibial Tissue-Specific: 1216 h2 > 0.1 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Specific: 449 non-zero h2 CI genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Specific: 17007 h2 > 0 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Specific: 8604 h2 > 0.01 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Specific: 3589 h2 > 0.05 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Specific: 1097 h2 > 0.1 genes"
## [1] "Thyroid Tissue-Specific: 442 non-zero h2 CI genes"
## [1] "Thyroid Tissue-Specific: 17007 h2 > 0 genes"
## [1] "Thyroid Tissue-Specific: 7757 h2 > 0.01 genes"
## [1] "Thyroid Tissue-Specific: 3141 h2 > 0.05 genes"
## [1] "Thyroid Tissue-Specific: 1010 h2 > 0.1 genes"
## [1] "WholeBlood Tissue-Specific: 490 non-zero h2 CI genes"
## [1] "WholeBlood Tissue-Specific: 17007 h2 > 0 genes"
## [1] "WholeBlood Tissue-Specific: 8504 h2 > 0.01 genes"
## [1] "WholeBlood Tissue-Specific: 2918 h2 > 0.05 genes"
## [1] "WholeBlood Tissue-Specific: 819 h2 > 0.1 genes"
## [1] "CrossTissue Tissue-Wide: 2938 non-zero h2 CI genes"
## [1] "CrossTissue Tissue-Wide: 17007 h2 > 0 genes"
## [1] "CrossTissue Tissue-Wide: 10282 h2 > 0.01 genes"
## [1] "CrossTissue Tissue-Wide: 5042 h2 > 0.05 genes"
## [1] "CrossTissue Tissue-Wide: 2715 h2 > 0.1 genes"
## [1] "AdiposeSubcutaneous Tissue-Wide: 1127 non-zero h2 CI genes"
## [1] "AdiposeSubcutaneous Tissue-Wide: 17007 h2 > 0 genes"
## [1] "AdiposeSubcutaneous Tissue-Wide: 6236 h2 > 0.01 genes"
## [1] "AdiposeSubcutaneous Tissue-Wide: 3407 h2 > 0.05 genes"
## [1] "AdiposeSubcutaneous Tissue-Wide: 1736 h2 > 0.1 genes"
## [1] "ArteryTibial Tissue-Wide: 1171 non-zero h2 CI genes"
## [1] "ArteryTibial Tissue-Wide: 17007 h2 > 0 genes"
## [1] "ArteryTibial Tissue-Wide: 6314 h2 > 0.01 genes"
## [1] "ArteryTibial Tissue-Wide: 3546 h2 > 0.05 genes"
## [1] "ArteryTibial Tissue-Wide: 1889 h2 > 0.1 genes"
## [1] "HeartLeftVentricle Tissue-Wide: 749 non-zero h2 CI genes"
## [1] "HeartLeftVentricle Tissue-Wide: 17007 h2 > 0 genes"
## [1] "HeartLeftVentricle Tissue-Wide: 6352 h2 > 0.01 genes"
## [1] "HeartLeftVentricle Tissue-Wide: 3865 h2 > 0.05 genes"
## [1] "HeartLeftVentricle Tissue-Wide: 2179 h2 > 0.1 genes"
## [1] "Lung Tissue-Wide: 929 non-zero h2 CI genes"
## [1] "Lung Tissue-Wide: 17007 h2 > 0 genes"
## [1] "Lung Tissue-Wide: 6331 h2 > 0.01 genes"
## [1] "Lung Tissue-Wide: 3387 h2 > 0.05 genes"
## [1] "Lung Tissue-Wide: 1693 h2 > 0.1 genes"
## [1] "MuscleSkeletal Tissue-Wide: 1063 non-zero h2 CI genes"
## [1] "MuscleSkeletal Tissue-Wide: 17007 h2 > 0 genes"
## [1] "MuscleSkeletal Tissue-Wide: 6049 h2 > 0.01 genes"
## [1] "MuscleSkeletal Tissue-Wide: 2814 h2 > 0.05 genes"
## [1] "MuscleSkeletal Tissue-Wide: 1331 h2 > 0.1 genes"
## [1] "NerveTibial Tissue-Wide: 1466 non-zero h2 CI genes"
## [1] "NerveTibial Tissue-Wide: 17006 h2 > 0 genes"
## [1] "NerveTibial Tissue-Wide: 6594 h2 > 0.01 genes"
## [1] "NerveTibial Tissue-Wide: 4057 h2 > 0.05 genes"
## [1] "NerveTibial Tissue-Wide: 2399 h2 > 0.1 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Wide: 1198 non-zero h2 CI genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Wide: 17007 h2 > 0 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Wide: 6504 h2 > 0.01 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Wide: 3530 h2 > 0.05 genes"
## [1] "SkinSunExposed(Lowerleg) Tissue-Wide: 1809 h2 > 0.1 genes"
## [1] "Thyroid Tissue-Wide: 1327 non-zero h2 CI genes"
## [1] "Thyroid Tissue-Wide: 17006 h2 > 0 genes"
## [1] "Thyroid Tissue-Wide: 6590 h2 > 0.01 genes"
## [1] "Thyroid Tissue-Wide: 3758 h2 > 0.05 genes"
## [1] "Thyroid Tissue-Wide: 2082 h2 > 0.1 genes"
## [1] "WholeBlood Tissue-Wide: 945 non-zero h2 CI genes"
## [1] "WholeBlood Tissue-Wide: 17007 h2 > 0 genes"
## [1] "WholeBlood Tissue-Wide: 5426 h2 > 0.01 genes"
## [1] "WholeBlood Tissue-Wide: 2586 h2 > 0.05 genes"
## [1] "WholeBlood Tissue-Wide: 1261 h2 > 0.1 genes"
write(data$ensid, file=out.dir %&% "full_tested_ensid_list.txt",ncolumns=1)

Define known genes for 7 WTCCC diseases based on the GWAS catalog and make list of ALL genes in catalog

  #catalog used:
  gwasfile <- my.dir %&% 'gwas_catalog_v1.0-downloaded_2015-06-02.tsv'
#  gwas <- data.frame(read.table(gwasfile,header=TRUE,sep='\t',quote="",comment.char="",as.is=TRUE)) #for reference
  runPERL <- "perl " %&% my.dir %&% "24_define_disease_genes.pl " %&% gwasfile
  system(runPERL)

Disease gene enrichment in top h2 genes by tissue

lci<-function(x) quantile(x, c(.025, 0.975),na.rm=T)[[1]]
uci<-function(x) quantile(x, c(.025, 0.975),na.rm=T)[[2]]
##is testvec signficanty enriched for setvec? make n samples of size(testvec) from fullvec and count overlap
enrichment <- function(setvec, testvec, fullvec, nperms = 1000){
  obs <- length(testvec[testvec %in% setvec])
  counts <- vector()
  for(i in 1:nperms){
    rantest <- base::sample(fullvec,length(testvec),replace=FALSE)
    cnt <- length(rantest[rantest %in% setvec])
    counts <- c(counts,cnt)
  }
  empp <- mean(counts>obs)
  meanc <- mean(counts)
  lc <- lci(counts)
  uc <- uci(counts)
  return(c(obs,meanc,lc,uc,empp))
}


  
set.seed(42)
fullgenelist <- as.character(ts$AssociatedGeneName)

dislist <- c("BD","CAD","HT","T1D","T2D","CD","RA","ALL")
nperms <- 10000

for(thresh in c("non-zeroCI","h2_0.1","h2_0.05","h2_0.01")){
  results <- data.frame(type=character(0),dis=character(0),tis=character(0),obsOverlap=double(0),meanOverlap=double(0),lCI=double(0),uCI=double(0),empPval=double(0))
  for(tistype in c("Tissue-Specific","Tissue-Wide")){
    for(i in seq_along(dislist)){
      dis <- dislist[i]
      disgenes <- scan(out.dir %&% "gwas." %&% dis %&% ".tsv","character")
      for(j in seq_along(tislist)){
        tis <- tislist[j]
        tisgenes <- scan(out.dir %&% tis %&% '_' %&% tistype %&% '_' %&% thresh %&% 'genes_gene_list.txt','character')
        res <- enrichment(disgenes,tisgenes,fullgenelist,nperms=nperms)
        resvec <- data.frame(type=tistype,dis=dis,tis=tis,obsOverlap=res[1],meanOverlap=res[2],lCI=res[3],uCI=res[4],empPval=res[5])
        results <- rbind(results,resvec)
      }
    }
  }
  sortres <- arrange(results,empPval)
  write.table(sortres,file=out.dir %&% "GWAS_catalog_disease_gene_enrichment_in_" %&% thresh %&% "_genes_by_tissue_" %&% nperms %&% "perms.txt",row.names=FALSE,quote=FALSE)
  print("GWAS catalog disease gene enrichment in " %&% thresh %&% " genes by tissue " %&% nperms %&% " perms:")
  print(head(sortres,n=20L))
}
## [1] "GWAS catalog disease gene enrichment in non-zeroCI genes by tissue 10000 perms:"
##               type dis                 tis obsOverlap meanOverlap lCI uCI
## 1  Tissue-Specific ALL                Lung        129    102.1142  88 117
## 2  Tissue-Specific T2D                Lung          9      2.1773   0   6
## 3  Tissue-Specific  CD      MuscleSkeletal         19      9.5357   4  16
## 4  Tissue-Specific  BD             Thyroid         17      8.2913   3  14
## 5  Tissue-Specific ALL        ArteryTibial        165    140.1439 123 157
## 6  Tissue-Specific  HT                Lung          3      0.5509   0   2
## 7  Tissue-Specific ALL  HeartLeftVentricle        160    136.3636 120 153
## 8  Tissue-Specific T2D AdiposeSubcutaneous          6      2.0594   0   5
## 9  Tissue-Specific T1D          WholeBlood          7      2.7214   0   6
## 10 Tissue-Specific  HT  HeartLeftVentricle          3      0.7396   0   3
## 11 Tissue-Specific T2D  HeartLeftVentricle          7      2.9590   0   7
## 12 Tissue-Specific CAD  HeartLeftVentricle          8      3.5693   1   8
## 13     Tissue-Wide  BD             Thyroid         36     24.9775  16  35
## 14 Tissue-Specific T2D             Thyroid          8      4.3519   1   9
## 15 Tissue-Specific  RA                Lung          5      2.3356   0   6
## 16 Tissue-Specific  RA  HeartLeftVentricle          6      3.1070   0   7
## 17 Tissue-Specific ALL             Thyroid        219    202.3259 182 223
## 18 Tissue-Specific  BD        ArteryTibial          9      5.7380   2  11
## 19 Tissue-Specific  RA          WholeBlood          8      5.1027   1  10
## 20 Tissue-Specific T1D  HeartLeftVentricle          3      1.6345   0   4
##    empPval
## 1   0.0001
## 2   0.0002
## 3   0.0014
## 4   0.0015
## 5   0.0015
## 6   0.0024
## 7   0.0027
## 8   0.0049
## 9   0.0054
## 10  0.0055
## 11  0.0111
## 12  0.0120
## 13  0.0120
## 14  0.0275
## 15  0.0331
## 16  0.0402
## 17  0.0478
## 18  0.0643
## 19  0.0708
## 20  0.0794
## [1] "GWAS catalog disease gene enrichment in h2_0.1 genes by tissue 10000 perms:"
##               type dis                      tis obsOverlap meanOverlap
## 1  Tissue-Specific  HT       HeartLeftVentricle         11      3.9737
## 2  Tissue-Specific  BD             ArteryTibial         28     16.7268
## 3  Tissue-Specific  BD                  Thyroid         30     18.9886
## 4  Tissue-Specific T2D                  Thyroid         18      9.9539
## 5  Tissue-Specific  CD           MuscleSkeletal         23     13.6412
## 6  Tissue-Specific ALL                  Thyroid        498    462.4593
## 7  Tissue-Specific ALL                     Lung        396    365.0230
## 8  Tissue-Specific T2D      AdiposeSubcutaneous         12      6.3959
## 9  Tissue-Specific T1D               WholeBlood          9      4.5149
## 10 Tissue-Specific T2D SkinSunExposed(Lowerleg)         17     10.8103
## 11     Tissue-Wide  BD                  Thyroid         50     39.2137
## 12 Tissue-Specific ALL             ArteryTibial        433    408.3995
## 13 Tissue-Specific  HT                     Lung          4      1.9382
## 14 Tissue-Specific ALL           MuscleSkeletal        304    284.5559
## 15 Tissue-Specific CAD      AdiposeSubcutaneous         12      7.8481
## 16 Tissue-Specific ALL      AdiposeSubcutaneous        317    297.4614
## 17 Tissue-Specific T2D              NerveTibial         17     12.0049
## 18 Tissue-Specific  BD      AdiposeSubcutaneous         17     12.1849
## 19     Tissue-Wide  HT                  Thyroid          8      5.1605
## 20     Tissue-Wide  BD SkinSunExposed(Lowerleg)         42     34.1768
##        lCI uCI empPval
## 1    1.000   8  0.0005
## 2    9.000  25  0.0036
## 3   11.000  27  0.0051
## 4    4.000  16  0.0052
## 5    7.000  21  0.0061
## 6  432.000 492  0.0114
## 7  338.000 392  0.0119
## 8    2.000  12  0.0123
## 9    1.000   9  0.0146
## 10   5.000  17  0.0248
## 11  28.000  51  0.0307
## 12 380.000 437  0.0401
## 13   0.000   5  0.0408
## 14 261.000 308  0.0473
## 15   3.000  14  0.0517
## 16 273.975 322  0.0544
## 17   6.000  19  0.0547
## 18   6.000  19  0.0643
## 19   1.000  10  0.0669
## 20  24.000  45  0.0685
## [1] "GWAS catalog disease gene enrichment in h2_0.05 genes by tissue 10000 perms:"
##               type dis                      tis obsOverlap meanOverlap
## 1  Tissue-Specific  HT                     Lung         15      6.7802
## 2      Tissue-Wide T2D             ArteryTibial         52     34.9891
## 3  Tissue-Specific ALL             ArteryTibial       1507   1430.8166
## 4  Tissue-Specific ALL                  Thyroid       1500   1438.3020
## 5      Tissue-Wide  HT             ArteryTibial         15      8.8099
## 6  Tissue-Specific  BD              NerveTibial         83     65.6931
## 7  Tissue-Specific ALL                     Lung       1305   1253.4985
## 8  Tissue-Specific  CD             ArteryTibial         83     68.4924
## 9      Tissue-Wide ALL           MuscleSkeletal       1333   1287.7332
## 10 Tissue-Specific T2D                     Lung         35     27.0397
## 11 Tissue-Specific CAD      AdiposeSubcutaneous         38     29.7497
## 12     Tissue-Wide ALL             ArteryTibial       1669   1623.8195
## 13 Tissue-Specific  HT       HeartLeftVentricle         14      9.8989
## 14 Tissue-Specific ALL           MuscleSkeletal       1115   1078.7831
## 15 Tissue-Specific ALL       HeartLeftVentricle       1883   1836.9226
## 16 Tissue-Specific  HT           MuscleSkeletal          9      5.8051
## 17     Tissue-Wide  HT                  Thyroid         13      9.3239
## 18 Tissue-Specific ALL SkinSunExposed(Lowerleg)       1682   1642.5537
## 19 Tissue-Specific T2D                  Thyroid         38     30.9843
## 20 Tissue-Specific ALL              NerveTibial       1637   1598.3363
##     lCI  uCI empPval
## 1     3   12  0.0004
## 2    25   46  0.0008
## 3  1381 1480  0.0010
## 4  1389 1486  0.0051
## 5     4   14  0.0077
## 6    52   80  0.0082
## 7  1207 1300  0.0154
## 8    55   83  0.0238
## 9  1240 1336  0.0323
## 10   18   37  0.0395
## 11   20   40  0.0418
## 12 1572 1676  0.0434
## 13    5   15  0.0480
## 14 1034 1122  0.0499
## 15 1782 1892  0.0501
## 16    2   10  0.0601
## 17    4   15  0.0635
## 18 1591 1695  0.0666
## 19   22   41  0.0686
## 20 1547 1650  0.0687
## [1] "GWAS catalog disease gene enrichment in h2_0.01 genes by tissue 10000 perms:"
##               type dis                 tis obsOverlap meanOverlap      lCI
## 1  Tissue-Specific ALL        ArteryTibial       3670   3560.6350 3498.000
## 2  Tissue-Specific  HT  HeartLeftVentricle         27     19.2386   13.000
## 3      Tissue-Wide ALL        ArteryTibial       2961   2889.8261 2829.000
## 4  Tissue-Specific ALL         NerveTibial       3642   3569.2810 3507.000
## 5      Tissue-Wide  BD         NerveTibial        141    124.0499  107.000
## 6  Tissue-Specific  BD      MuscleSkeletal        156    138.8364  122.000
## 7      Tissue-Wide T2D        ArteryTibial         74     62.3432   50.000
## 8      Tissue-Wide  HT        ArteryTibial         21     15.5968   10.000
## 9  Tissue-Specific ALL  HeartLeftVentricle       3626   3568.2826 3506.000
## 10 Tissue-Specific ALL                Lung       3326   3270.1042 3205.975
## 11 Tissue-Specific  HT         NerveTibial         24     19.2605   13.000
## 12     Tissue-Wide  HT             Thyroid         21     16.3225   10.000
## 13     Tissue-Wide  BD AdiposeSubcutaneous        130    117.2681  101.000
## 14 Tissue-Specific ALL             Thyroid       3600   3550.8891 3487.000
## 15     Tissue-Wide T2D             Thyroid         74     65.0499   53.000
## 16 Tissue-Specific ALL AdiposeSubcutaneous       3207   3161.6611 3099.000
## 17 Tissue-Specific ALL      MuscleSkeletal       3419   3374.3486 3311.000
## 18     Tissue-Wide CAD      MuscleSkeletal         82     72.9027   60.000
## 19 Tissue-Specific  CD AdiposeSubcutaneous        164    151.5080  133.000
## 20     Tissue-Wide  CD          WholeBlood        130    118.8994  102.000
##         uCI empPval
## 1  3625.000  0.0003
## 2    26.000  0.0052
## 3  2952.000  0.0108
## 4  3633.000  0.0140
## 5   141.000  0.0229
## 6   156.000  0.0233
## 7    75.000  0.0286
## 8    22.000  0.0301
## 9  3631.000  0.0350
## 10 3333.000  0.0379
## 11   26.000  0.0501
## 12   23.000  0.0509
## 13  134.000  0.0614
## 14 3615.025  0.0646
## 15   78.000  0.0692
## 16 3224.000  0.0740
## 17 3438.000  0.0812
## 18   86.000  0.0822
## 19  170.000  0.0852
## 20  136.000  0.0960

distribution of h2 for disease vs non disease

dislist <- c("BD","CAD","HT","T1D","T2D","CD","RA","ALL")
tislist <- c("CrossTissue","AdiposeSubcutaneous","ArteryTibial","HeartLeftVentricle","Lung","MuscleSkeletal","NerveTibial","SkinSunExposed(Lowerleg)","Thyroid","WholeBlood")
typelist<-c("Tissue-Specific","Tissue-Wide")

for(thresh in c(0.1,0.05,0)){
  for(tistype in typelist){
    for(tis in tislist){
      info <- read.table(out.dir %&% tis %&% '_' %&% tistype %&% '_h2_' %&% thresh %&% 'genes_info.txt',header=T)
      finaldf <- data.frame(AssociatedGeneName=character(0),EnsemblGeneID=character(0),h2=double(0),se=double(0),ensid=character(0),diseaseGene=logical(0L),disease=character(0))
      for(dis in dislist){
        setvec <- scan(out.dir %&% "gwas." %&% dis %&% ".tsv","character")
        disinfo <- info %>% mutate(diseaseGene=(info[,1] %in% setvec),disease=dis)
        colnames(disinfo) <- c("AssociatedGeneName","EnsemblGeneID","h2","se","ensid","diseaseGene","disease")
        finaldf <- rbind(finaldf,disinfo)
      }
      p<-ggplot(finaldf,aes(x=finaldf[,3],fill=diseaseGene,color=diseaseGene)) + facet_wrap(~disease,ncol=2) + geom_density(alpha=0.3) + xlab("h2") + ggtitle(tistype %&% ' ' %&% tis %&% ' h2 > ' %&% thresh)
      print(p)
      p<-ggplot(finaldf,aes(y=finaldf[,3],x=diseaseGene)) + facet_wrap(~disease,ncol=4) + geom_jitter(aes(colour=diseaseGene),alpha=0.3,position = position_jitter(width = .15)) + geom_boxplot(alpha=0,outlier.size=NA) + xlab("diseaseGene") + ylab("h2") + ggtitle(tistype %&% ' ' %&% tis %&% ' h2 > ' %&% thresh) +theme_bw() + theme(legend.position="none") 
      print(p)
    }
  }
}